E-CELL: software environment for whole-cell simulation

The hard cell: From proteomics to a whole cell model

Proteomics has provided a wealth of data related to the nature of the proteome, including subcellular location, copy number, interaction partners and protein complexes. This raises the question of whether it is feasible to combine these data, together with other data related to overall cellular structure, to construct a static picture of the cell. In this minireview, we discuss these data, and the issues of turning them into whole cell models.

RMBNToolbox: random models for biochemical networks

BACKGROUND

There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models.

RESULTS

We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language.

CONCLUSION

While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.

Taking the example of computer systems engineering for the analysis of biological cell systems

In this paper, we discuss the potential for the use of engineering methods that were originally developed for the design of embedded computer systems, to analyse biological cell systems. For embedded systems as well as for biological cell systems, design is a feature that defines their identity. The assembly of different components in designs of both systems can vary widely. In contrast to the biology domain, the computer engineering domain has the opportunity to quickly evaluate design options and consequences of its systems by methods for computer aided design and in particular design space exploration. We argue that there are enough concrete similarities between the two systems to assume that the engineering methodology from the computer systems domain, and in particular that related to embedded systems, can be applied to the domain of cellular systems. This will help to understand the myriad of different design options cellular systems have. First we compare computer systems with cellular systems. Then, we discuss exactly what features of engineering methods could aid researchers with the analysis of cellular systems, and what benefits could be gained.

Computational systems biology in drug discovery and development: methods and applications

Computational systems biology is an emerging field in biological simulation that attempts to model or simulate intra- and intercellular events using data gathered from genomic, proteomic or metabolomic experiments. The need to model complex temporal and spatiotemporal processes at many different scales has led to the emergence of numerous techniques, including systems of differential equations, Petri nets, cellular automata simulators, agent-based models and pi calculus. This review provides a brief summary and an assessment of most of these approaches. It also provides examples of how these methods are being used to facilitate drug discovery and development.

Roles of hemoglobin Allostery in hypoxia-induced metabolic alterations in erythrocytes: simulation and its verification by metabolome analysis

When erythrocytes are exposed to hypoxia, hemoglobin (Hb) stabilizes in the T-state by capturing 2,3-bisphosphoglycerate. This process could reduce the intracellular pool of glycolytic substrates, jeopardizing cellular energetics. Recent observations suggest that hypoxia-induced activation of glycolytic enzymes is correlated with their release from Band III (BIII) on the cell membrane. Based on these data, we developed a mathematical model of erythrocyte metabolism and compared hypoxia-induced differences in predicted activities of the enzymes, their products, and cellular energetics between models with and without the interaction of Hb with BIII. The models predicted that the allostery-dependent Hb interaction with BIII accelerates consumption of upstream glycolytic substrates such as glucose 6-phosphate and increases downstream products such as phosphoenolpyruvate. This prediction was consistent with metabolomic data from capillary electrophoresis mass spectrometry. The hypoxia-induced alterations in the metabolites resulted from acceleration of glycolysis, as judged by increased conversion of [(13)C]glucose to [(13)C]lactate. The allostery-dependent interaction of Hb with BIII appeared to contribute not only to maintenance of energy charge but also to further synthesis of 2,3-bisphosphoglycerate, which could help sustain stabilization of T-state Hb during hypoxia. Furthermore, such an activation of glycolysis was not observed when Hb was stabilized in R-state by treating the cells with CO. These results suggest that Hb allostery in erythrocytes serves as an O(2)-sensing trigger that drives glycolytic acceleration to stabilize intracellular energetics and promote the ability to release O(2) from the cells.

A software environment for simulators suitable for complex biological analysis

In order to understand the function of biological elements and their interactions, computer analysis and simulation is an essential technique. For higher research efficiency, it is important to provide a system framework for constructing biological simulation systems that handle multiple phenomena. This paper proposes "DynaBioS", a comprehensive system framework for complex biological simulations. This framework consists of three main features, a component-based architecture, a customizable system operation, and an exchangeable model set. The system based on DynaBioS consists of a simulation core and system components. The system components are sub-simulators for individual functional factors and utility modules corresponding to specific information technologies. The simulation core manages and controls all components, in accordance with a simulation scenario and a simulation model. The DynaBioS makes it possible to implement different simulation systems by combining individual functional components, specific interactions and different simulation models. The versatility of DynaBioS is shown by two examples, a heart pumping simulator and a parameter optimization system for physiological models.

Toward whole cell modeling and simulation: comprehensive functional genomics through the constraint-based approach

The increasing availability of various system-level, or so-called 'omics', datasets, in concert with existing data from the primary research literature, is facilitating the development of genome-scale metabolic models for many organisms. By incorporating the metabolic reaction stoichiometry as well as other physicochemical properties into systemic network reconstructions, these models account for the constraints that restrict an organism's phenotypic behavior. Accordingly, unlike many contemporary modeling strategies, this constraint-based modeling approach does not attempt to predict network behavior exactly; rather, it seeks to clearly distinguish those network states that a system can achieve from those that it cannot. A variety of analytical tools have been designed and developed to probe these models, thus enabling studies that investigate the metabolic capabilities of a number of organisms, that generate and test experimental hypotheses, and that predict accurately metabolic phenotypes and evolutionary outcomes. This chapter introduces the concepts that underlie the constraint-based modeling approach, and describes several of its applications with an emphasis on those potentially relevant to the drug development field. In addition, while this chapter focuses on the primary application of the constraint-based approach to date, namely in modeling metabolic networks, the latter sections of the chapter discuss its relatively recent application to modeling other cellular systems. Finally, the chapter concludes with an assessment of future directions focusing on the efforts that will be required to utilize the constraint-based approach in generating a holistic model of a viable organism.

Probability-based model of protein-protein interactions on biological timescales

BACKGROUND

Simulation methods can assist in describing and understanding complex networks of interacting proteins, providing fresh insights into the function and regulation of biological systems. Recent studies have investigated such processes by explicitly modelling the diffusion and interactions of individual molecules. In these approaches, two entities are considered to have interacted if they come within a set cutoff distance of each other.

RESULTS

In this study, a new model of bimolecular interactions is presented that uses a simple, probability-based description of the reaction process. This description is well-suited to simulations on timescales relevant to biological systems (from seconds to hours), and provides an alternative to the previous description given by Smoluchowski. In the present approach (TFB) the diffusion process is explicitly taken into account in generating the probability that two freely diffusing chemical entities will interact within a given time interval. It is compared to the Smoluchowski method, as modified by Andrews and Bray (AB).

CONCLUSION

When implemented, the AB & TFB methods give equivalent results in a variety of situations relevant to biology. Overall, the Smoluchowski method as modified by Andrews and Bray emerges as the most simple, robust and efficient method for simulating biological diffusion-reaction processes currently available.

Synthetic biology projects in vitro

Advances in the in vitro synthesis and evolution of DNA, RNA, and polypeptides are accelerating the construction of biopolymers, pathways, and organisms with novel functions. Known functions are being integrated and debugged with the aim of synthesizing life-like systems. The goals are knowledge, tools, smart materials, and therapies.

Cell++—simulating biochemical pathways

MOTIVATION

With the generation of a wealth of information, detailing cellular components, their functions and interactions, there is a growing need for the development of new computational tools capable of interpreting these data within spatial and dynamic contexts. Here, we introduce Cell++, a novel stochastic simulation environment with the capacity to study a wide variety of biochemical processes within a spatial context.

RESULTS

Focusing on three case studies, we highlight the potential impact of spatial organization in the evolution and engineering of signaling and metabolic pathways. In addition to altering signaling and metabolic efficiency, simulations also demonstrated features consistent with the phenomenon of metabolic channeling.

AVAILABILITY

Cell++ is licensed under the GNU general public license (GPL) and has been successfully implemented under Linux and IRIX operating systems. Source code together with a simple tutorial is available at http://www.compsysbio.org/CellSim/.

COPASI--a COmplex PAthway SImulator

MOTIVATION

Simulation and modeling is becoming a standard approach to understand complex biochemical processes. Therefore, there is a big need for software tools that allow access to diverse simulation and modeling methods as well as support for the usage of these methods.

RESULTS

Here, we present COPASI, a platform-independent and user-friendly biochemical simulator that offers several unique features. We discuss numerical issues with these features; in particular, the criteria to switch between stochastic and deterministic simulation methods, hybrid deterministic-stochastic methods, and the importance of random number generator numerical resolution in stochastic simulation.

AVAILABILITY

The complete software is available in binary (executable) for MS Windows, OS X, Linux (Intel) and Sun Solaris (SPARC), as well as the full source code under an open source license from http://www.copasi.org.

Co-occurrence of resonant activation and noise-enhanced stability in a model of cancer growth in the presence of immune response

We investigate a stochastic version of a simple enzymatic reaction which follows the generic Michaelis-Menten kinetics. At sufficiently high concentrations of reacting species, that represent here populations of cells involved in cancerous proliferation and cytotoxic response of the immune system, the overall kinetics can be approximated by a one-dimensional overdamped Langevin equation. The modulating activity of the immune response is here modeled as a dichotomous random process of the relative rate of neoplastic cell destruction. We discuss physical aspects of environmental noises acting in such a system, pointing out the possibility of coexistence of dynamical regimes where noise-enhanced stability and resonant activation phenomena can be observed together. We explain the underlying mechanisms by analyzing the behavior of the variance of first passage times as a function of the noise intensity.

Towards synthesis of a minimal cell

Construction of a chemical system capable of replication and evolution, fed only by small molecule nutrients, is now conceivable. This could be achieved by stepwise integration of decades of work on the reconstitution of DNA, RNA and protein syntheses from pure components. Such a minimal cell project would initially define the components sufficient for each subsystem, allow detailed kinetic analyses and lead to improved in vitro methods for synthesis of biopolymers, therapeutics and biosensors. Completion would yield a functionally and structurally understood self-replicating biosystem. Safety concerns for synthetic life will be alleviated by extreme dependence on elaborate laboratory reagents and conditions for viability. Our proposed minimal genome is 113 kbp long and contains 151 genes. We detail building blocks already in place and major hurdles to overcome for completion.

Structural kinetic modeling of metabolic networks

To develop and investigate detailed mathematical models of metabolic processes is one of the primary challenges in systems biology. However, despite considerable advance in the topological analysis of metabolic networks, kinetic modeling is still often severely hampered by inadequate knowledge of the enzyme-kinetic rate laws and their associated parameter values. Here we propose a method that aims to give a quantitative account of the dynamical capabilities of a metabolic system, without requiring any explicit information about the functional form of the rate equations. Our approach is based on constructing a local linear model at each point in parameter space, such that each element of the model is either directly experimentally accessible or amenable to a straightforward biochemical interpretation. This ensemble of local linear models, encompassing all possible explicit kinetic models, then allows for a statistical exploration of the comprehensive parameter space. The method is exemplified on two paradigmatic metabolic systems: the glycolytic pathway of yeast and a realistic-scale representation of the photosynthetic Calvin cycle.

Constructing explanatory process models from biological data and knowledge

OBJECTIVE

We address the task of inducing explanatory models from observations and knowledge about candidate biological processes, using the illustrative problem of modeling photosynthesis regulation.

METHODS

We cast both models and background knowledge in terms of processes that interact to account for behavior. We also describe IPM, an algorithm for inducing quantitative process models from such input.

RESULTS

We demonstrate IPM's use both on photosynthesis and on a second domain, biochemical kinetics, reporting the models induced and their fit to observations.

CONCLUSION

We consider the generality of our approach, discuss related research on biological modeling, and suggest directions for future work.

A bioinformatician's view of the metabolome

The study of a collection of metabolites as a whole (metabolome), as opposed to isolated small molecules, is a fast-growing field promising to take us one step further towards understanding cell biology, and relating the genetic capabilities of an organism to its observed phenotype. The new sciences of metabolomics and metabonomics can exploit a variety of existing experimental and computational methods, but they also require new technology that can deal with both the amount and the diversity of the data relating to the rich world of metabolites. More specifically, the collaboration between bioinformaticians and chemoinformaticians promises to advance our view of cognate molecules, by shedding light on their atomic structure and properties. Modelling of the interactions of metabolites with other entities in the cell, and eventually complete modelling of reaction pathways will be essential for analysis of the experimental data, and prediction of an organism's response to environmental challenges.

Modeling Hsp70-mediated protein folding

The Hsp70 chaperone system is the major molecular chaperone system that assists protein-folding processes in all cells. To understand these processes, we analyzed the kinetic characteristics of the Escherichia coli homologs of this chaperone system during folding of a denatured protein using computer simulations and compared the results with in vitro refolding experiments. Rate constants used for the model were derived from recent literature or were determined and scrutinized for their applicability to the refolding reaction. Our simulation results are consistent with reported laboratory experiments, not only simulating the refolding reaction of wild-type proteins but also the behavior of mutant variants. Variation of kinetic parameters and concentrations of components of the Hsp70 system demonstrate the robustness of the chaperone system in assisting protein folding. Furthermore, the importance of the synergistic stimulation of the ATPase activity of Hsp70 is demonstrated. The limitations of our kinetic model indicate sore spots in our understanding of this chaperone system. Our model provides a platform for further research on chaperone action and the mechanism of chaperone-assisted refolding of denatured proteins.

Qualitative analysis of the relation between DNA microarray data and behavioral models of regulation networks

We introduce a mathematical framework that allows to test the compatibility between differential data and knowledge on genetic and metabolic interactions. Within this framework, a behavioral model is represented by a labeled oriented interaction graph; its predictions can be compared to experimental data. The comparison is qualitative and relies on a system of linear qualitative equations derived from the interaction graph. We show how to partially solve the qualitative system, how to identify incompatibilities between the model and the data, and how to detect competitions in the biological processes that are modeled. This approach can be used for the analysis of transcriptomic, metabolic or proteomic data.

Computational modelling of the receptor-tyrosine-kinase-activated MAPK pathway

The MAPK (mitogen-activated protein kinase) pathway is one of the most important and intensively studied signalling pathways. It is at the heart of a molecular-signalling network that governs the growth, proliferation, differentiation and survival of many, if not all, cell types. It is de-regulated in various diseases, ranging from cancer to immunological, inflammatory and degenerative syndromes, and thus represents an important drug target. Over recent years, the computational or mathematical modelling of biological systems has become increasingly valuable, and there is now a wide variety of mathematical models of the MAPK pathway which have led to some novel insights and predictions as to how this system functions. In the present review we give an overview of the processes involved in modelling a biological system using the popular approach of ordinary differential equations. Focusing on the MAPK pathway, we introduce the features and functions of the pathway itself before comparing the available models and describing what new biological insights they have led to.

Computer modelling of antifolate inhibition of folate metabolism using hybrid functional petri nets

Antifolates are used in the treatment of various human malignancies and exert their cytotoxic activity by inhibiting folate-dependent enzymes resulting in disruption of DNA synthesis and cell death. Here we devised a computerized hybrid functional petri nets (HFPN) modelling of folate metabolism under physiological and antifolate inhibitory conditions. This HFPN modelling proved valid as a good agreement was found between the simulated steady-state concentrations of various reduced folates and those published for cell extracts; consistently, the simulation derived total folate pool size (11.3 microM) was identical to that published for cell extracts. In silico experiments were conducted to characterize the inhibitory profile of four distinct antifolates including methotrexate (MTX), tomudex, and LY309887, which inhibit dihydrofolate reductase (DHFR), thymidylate synthase (TS) and glycineamide ribonucleotide transformylase (GARTFase), respectively, as well as pemetrexed which has the capacity to inhibit all three enzymes. In order to assess the inhibitory activity of antifolates on purines and pyrimidines, the biosynthesis rates of IMP (20.53 microM/min) and dTMP (23.8 microM/min) were first simulated. Whereas the biochemical inhibitory profile of MTX was characterized by increased dihydrofolate and decreased tetrahydrofolate (THF) concentrations, the remaining antifolates did not decrease THF levels. Furthermore, MTX was 766- and 10-fold more potent in decreasing the production rates of IMP and dTMP, respectively, than pemetrexed. LY309887 indirectly decreased the rate of dTMP production by reducing the levels of 5-CH2-THF, a folate cofactor for TS. Surprisingly, pemetrexed failed to inhibit DHFR even at high concentrations. This HFPN-based simulation offers an inexpensive, user-friendly, rapid and reliable means of pre-clinical evaluation of the inhibitory profiles of antifolates.

Phenomenological and molecular-level Petri net modeling and simulation of long-term potentiation

Petri net-based modeling methods have been used in many research projects to represent biological systems. Among these, the hybrid functional Petri net (HFPN) was developed especially for biological modeling in order to provide biologists with a more intuitive Petri net-based method. In the literature, HFPNs are used to represent kinetic models at the molecular level. We present two models of long-term potentiation previously represented by differential equations which we have transformed into HFPN models: a phenomenological synapse model and a molecular-level model of the CaMKII regulation pathway. Through simulation, we obtained results similar to those of previous studies using these models. Our results open the way to a new type of modeling for systems biology where HFPNs are used to combine different levels of abstraction within one model. This approach can be useful in fully modeling a system at the molecular level when kinetic data is missing or when a full study of a system at the molecular level it is not within the scope of the research.

A control study to evaluate a computer-based microarray experiment design recommendation system for gene-regulation pathways discovery

The main topic of this paper is evaluating a system that uses the expected value of experimentation for discovering causal pathways in gene expression data. By experimentation we mean both interventions (e.g., a gene knock-out experiment) and observations (e.g., passively observing the expression level of a "wild-type" gene). We introduce a system called GEEVE (causal discovery in Gene Expression data using Expected Value of Experimentation), which implements expected value of experimentation in discovering causal pathways using gene expression data. GEEVE provides the following assistance, which is intended to help biologists in their quest to discover gene-regulation pathways: Recommending which experiments to perform (with a focus on "knock-out" experiments) using an expected value of experimentation (EVE) method. Recommending the number of measurements (observational and experimental) to include in the experimental design, again using an EVE method. Providing a Bayesian analysis that combines prior knowledge with the results of recent microarray experimental results to derive posterior probabilities of gene regulation relationships. In recommending which experiments to perform (and how many times to repeat them) the EVE approach considers the biologist's preferences for which genes to focus the discovery process. Also, since exact EVE calculations are exponential in time, GEEVE incorporates approximation methods. GEEVE is able to combine data from knock-out experiments with data from wild-type experiments to suggest additional experiments to perform and then to analyze the results of those microarray experimental results. It models the possibility that unmeasured (latent) variables may be responsible for some of the statistical associations among the expression levels of the genes under study. To evaluate the GEEVE system, we used a gene expression simulator to generate data from specified models of gene regulation. Using the simulator, we evaluated the GEEVE system using a randomized control study that involved 10 biologists, some of whom used GEEVE and some of whom did not. The results show that biologists who used GEEVE reached correct causal assessments about gene regulation more often than did those biologists who did not use GEEVE. The GEEVE users also reached their assessments in a more cost-effective manner.

Dizzy: stochastic simulation of large-scale genetic regulatory networks

We describe Dizzy, a software tool for stochastically and deterministically modeling the spatially homogeneous kinetics of integrated large-scale genetic, metabolic, and signaling networks. Notable features include a modular simulation framework, reusable modeling elements, complex kinetic rate laws, multi-step reaction processes, steady-state noise estimation, and spatial compartmentalization.

Integrating genomics and metabolomics for engineering plant metabolic pathways

Plant metabolites are characterized by an enormous chemical diversity, every plant having its own complex set of metabolites. This variety poses analytical challenges, both for profiling multiple metabolites in parallel and for the quantitative analysis of selected metabolites. We are only just starting to understand the roles of these metabolites, many of them being involved in adaptations to specific ecological niches and some finding beneficial use (e.g. as pharmaceuticals). Spectacular advances in plant metabolomics offer new possibilities, together with the aid of systems biology, to explore the extraordinary complexity of the plant biochemical capacity. State-of-the art genomics tools can be combined with metabolic profiling to identify key genes that could be engineered for the production of improved crop plants.

In vitro, in vivo, in silico: computational systems in tissue engineering and regenerative medicine

The emergence of computational systems in tissue engineering and regenerative medicine is paralleling the rapid rise of new technology. Developments in software and hardware have allowed access to the huge data streams that are now available. The Human Genome Project led the way and opened many avenues for other branches of science and biology with extensive but contained databanks. The availability of vast amounts of data means nothing without the ability to integrate the information into a useful form. Tissue engineering has always been a practical science in which function and utility are key objectives. This review delineates key areas of this rapidly ascending branch of science and illustrates examples central to the successful integration of computational methods.

Systems biotechnology for strain improvement

Various high-throughput experimental techniques are routinely used for generating large amounts of omics data. In parallel, in silico modelling and simulation approaches are being developed for quantitatively analyzing cellular metabolism at the systems level. Thus informative high-throughput analysis and predictive computational modelling or simulation can be combined to generate new knowledge through iterative modification of an in silico model and experimental design. On the basis of such global cellular information we can design cells that have improved metabolic properties for industrial applications. This article highlights the recent developments in these systems approaches, which we call systems biotechnology, and discusses future prospects.

simBio: a Java package for the development of detailed cell models

Quantitative dynamic computer models, which integrate a variety of molecular functions into a cell model, provide a powerful tool to create and test working hypotheses. We have developed a new modeling tool, the simBio package (freely available from ), which can be used for constructing cell models, such as cardiac cells (the Kyoto model from Matsuoka et al., 2003, 2004 a, b, the LRd model from Faber and Rudy, 2000, and the Noble 98 model from Noble et al., 1998), epithelial cells (Strieter et al., 1990) and pancreatic beta cells (Magnus and Keizer, 1998). The simBio package is written in Java, uses XML and can solve ordinary differential equations. In an attempt to mimic biological functional structures, a cell model is, in simBio, composed of independent functional modules called Reactors, such as ion channels and the sarcoplasmic reticulum, and dynamic variables called Nodes, such as ion concentrations. The interactions between Reactors and Nodes are described by the graph theory and the resulting graph represents a blueprint of an intricate cellular system. Reactors are prepared in a hierarchical order, in analogy to the biological classification. Each Reactor can be composed or improved independently, and can easily be reused for different models. This way of building models, through the combination of various modules, is enabled through the use of object-oriented programming concepts. Thus, simBio is a straightforward system for the creation of a variety of cell models on a common database of functional modules.

Dynamic simulation of red blood cell metabolism and its application to the analysis of a pathological condition

Background

Cell simulation, which aims to predict the complex and dynamic behavior of living cells, is becoming a valuable tool. In silico models of human red blood cell (RBC) metabolism have been developed by several laboratories. An RBC model using the E-Cell simulation system has been developed. This prototype model consists of three major metabolic pathways, namely, the glycolytic pathway, the pentose phosphate pathway and the nucleotide metabolic pathway. Like the previous model by Joshi and Palsson, it also models physical effects such as osmotic balance. This model was used here to reconstruct the pathology arising from hereditary glucose-6-phosphate dehydrogenase (G6PD) deficiency, which is the most common deficiency in human RBC.

Results

Since the prototype model could not reproduce the state of G6PD deficiency, the model was modified to include a pathway for de novo glutathione synthesis and a glutathione disulfide (GSSG) export system. The de novo glutathione (GSH) synthesis pathway was found to compensate partially for the lowered GSH concentrations resulting from G6PD deficiency, with the result that GSSG could be maintained at a very low concentration due to the active export system.

Conclusion

The results of the simulation were consistent with the estimated situation of real G6PD-deficient cells. These results suggest that the de novo glutathione synthesis pathway and the GSSG export system play an important role in alleviating the consequences of G6PD deficiency.

CADLIVE dynamic simulator: direct link of biochemical networks to dynamic models

We have developed the CADLIVE (Computer-Aided Design of LIVing systEms) Simulator that provided a rule-based automatic way to convert biochemical network maps into dynamic models, which enables simulating their dynamics without going through all of the reactions down to the details of exact kinetic parameters. The simulator supports the biochemical reaction maps that are generated by the previously developed GUI editor. Notice that the part of the GUI editor had been previously published, but, as yet, not the simulator. To directly link biochemical network maps to dynamic simulation, we have created the strategy of three layers and two stages with the efficient conversion rules in an XML representation. This strategy divides a molecular network into three layers, i.e., gene, protein, and metabolic layers, and partitions the conversion process into two stages. Once a biochemical map is provided, CADLIVE automatically builds a mathematical model, thereby facilitating one to simulate and analyze it. In order to demonstrate the feasibility of CADLIVE, we analyzed the Escherichia coli nitrogen-assimilation system (64 equations with 64 variables) that consists of multiple and complicated negative and positive feedback loops. CADLIVE predicted that the glnK gene is responsible for hysteresis or reversibility of nitrogen-related (Ntr) gene expression with respect to the ammonia concentration, supporting the experimental observation of the runaway expression of the Ntr genes.

Complementary identification of multiple flux distributions and multiple metabolic pathways

Cell robustness and complexity have been recognized as unique features of biological systems. Such robustness and complexity of metabolic-reaction systems can be explored by discovering, or identifying, the multiple flux distributions (MFD) and redundant pathways that lead to a given external state; however, this is exceedingly cumbersome to accomplish. It is, therefore, highly desirable to establish an effective computational method for their identification, which, in turn, gives rise to a novel insight into the cellular function. An effective approach is proposed for complementarily identifying MFD in metabolic flux analysis and multiple metabolic pathways (MMP) in structural pathway analysis. This approach judiciously integrates flux balance analysis (FBA) based on linear programming and the graph-theoretic method for determining reaction pathways. A single metabolic pathway, with the concomitant flux distribution and the overall reaction manifesting itself as the desired phenotype under some environmental conditions, is determined by FBA from the initial candidate sequence of metabolic reactions. Subsequently, the graph-theoretic method recovers all feasible MMP and the corresponding MFD. The approach's efficacy is demonstrated by applying it to the in silico Escherichia coli model under various culture conditions. The resultant MMP and MFD attaining a unique external state reveal the surprising adaptability and robustness of the intricate cellular network as a key to cell survival against environmental or genetic changes. These results indicate that the proposed approach would be useful in facilitating drug discovery.

Sustained MAPK activation is dependent on continual NGF receptor regeneration

It still remains intriguing how signal specificity is achieved when different signals are relayed by the common intracellular signal transduction pathways. A well documented example for signal specificity determination is found in rat phaeochromocytoma PC12 cells where epidermal growth factor (EGF) stimulation produces a transient mitogen-activated protein kinase (MAPK) activation and leads to cell proliferation while nerve growth factor (NGF) initiates a sustained MAPK activation and induces cell differentiation. In this simulation, we demonstrated that NGF-induced sustained MAPK activation may mainly depend on continual regeneration of NGF receptors and that the presence of a small pool of surface receptors is enough to maintain a sustained MAPK activation. On the other hand, MAPK activation is not significantly sensitive to the half-life of internalized receptors and the levels of NGF-specific MAPK phosphatase MAP kinase phosphatase-3 (MKP-3), though cytoplasmic persistence of internalized NGF-bound receptors and the MKP-3 dependent feedback control also contribute to the sustaining of MAPK activation. These results are consistent with the recent experimental evidence that persistent tyrosine receptor kinase A (TrkA) activity is necessary to maintain transcription in the differentiating PC12 cells (Chang et al. 2003) and a sustained Src kinase activity is detected in response to NGF stimulation (Gatti 2003). It is suggested that sustained or transient MAPK activation induced by different growth factor and neurotrophins, which is crucial to their signaling specificity, could be satisfactorily accounted for by their specific receptor turnover kinetics rather than by the activation of specific downstream signaling cascades.

Simulating the G-protein cAMP pathway with a two-compartment reactive lattice gas

Cellular signaling (or signal transduction) is the process by which extracellular signals are converted into cellular responses. Mathematical models of signaling pathways may help understanding their general regulatory principles, as well as the roles of the different components often involved in more than one pathway.The aim of the present work is to describe a discrete model in time and space to study the dynamics of the population of molecules involved in a specific pathway. To this purpose, we use a multi-compartment stochastic cellular automaton to simulate one of the best understood cell signaling pathways: the GPCR-pathway.We then show the effect on the signal-carrying efficiency of condensing a molecular species, which is pivotal to the reaction pathway, in a closed region of the cytosol.We find that localization increases the robustness of the translocation mechanism only above a density threshold. For homogeneous settings, by increasing the concentration above a critical value, the translocation is obstructed by mere physical occupation constraints of the signal-carrying molecule. This conclusion is in line with previous findings, according to which in highly dense regions of the cytosol, convection by autocatalytic activation of adjacent molecules might be more efficient than diffusion as a main signal propagation mechanism.

Sensitivity, principal component and flux analysis applied to signal transduction: the case of epidermal growth factor mediated signaling

MOTIVATION

Novel high-throughput genomic and proteomic tools are allowing the integration of information from a range of biological assays into a single conceptual framework. This framework is often described as a network of biochemical reactions. We present strategies for the analysis of such networks.

RESULTS

The direct differential method is described for the systematic evaluation of scaled sensitivity coefficients in reaction networks. Principal component analysis, based on an eigenvalue-eigenvector analysis of the scaled sensitivity coefficient matrix, is applied to rank individual reactions in the network based on their effect on system output. When combined with flux analysis, sensitivity analysis allows model reduction or simplification. Using epidermal growth factor (EGF) mediated signaling and trafficking as an example of signal transduction, we demonstrate that sensitivity analysis quantitatively reveals the dependence of dual-phosphorylated extracellular signal-regulated kinase (ERK) concentration on individual reaction rate constants. It predicts that EGF mediated reactions proceed primarily via an Shc-dependent pathway. Further, it suggests that receptor internalization and endosomal signaling are important features regulating signal output only at low EGF dosages and at later times.

Analyzing the biology on the system level

Although various genome projects have provided us enormous static sequence information, understanding of the sophisticated biology continues to require integrating the computational modeling, system analysis, technology development for experiments, and quantitative experiments all together to analyze the biology architecture on various levels, which is just the origin of systems biology subject. This review discusses the object, its characteristics, and research attentions in systems biology, and summarizes the analysis methods, experimental technologies, research developments, and so on in the four key fields of systems biology—systemic structures, dynamics, control methods, and design principles.

AgentCell: a digital single-cell assay for bacterial chemotaxis

MOTIVATION

In recent years, single-cell biology has focused on the relationship between the stochastic nature of molecular interactions and variability of cellular behavior. To describe this relationship, it is necessary to develop new computational approaches at the single-cell level.

RESULTS

We have developed AgentCell, a model using agent-based technology to study the relationship between stochastic intracellular processes and behavior of individual cells. As a test-bed for our approach we use bacterial chemotaxis, one of the best characterized biological systems. In this model, each bacterium is an agent equipped with its own chemotaxis network, motors and flagella. Swimming cells are free to move in a 3D environment. Digital chemotaxis assays reproduce experimental data obtained from both single cells and bacterial populations.

Parallelising a model of bacterial interaction and evolution

Large simulations of bacterial colonies require huge amounts of computational time, the only way to achieve the necessary level of performance is with parallel computers and a suitably designed implementation that maps the problem onto the hardware. For real problems this mapping can be a non-trivial problem requiring careful consideration of the constraints in both the system being modelled and the hardware that executes that model. Here we describe an implementation of a system for modelling bacterial evolution that encompasses many physical scales. This system is composed entirely of individual entities all playing out a complex series of interactions. These individuals exist at the scale of the population of bacterial and at the gene product scale. This paper reports that it is possible to map a dynamic problem such as this onto fixed resources, for the most part making use of implicit multiplexing of resources provided by the OS and partitioning the problem to reduce communication time. Through this an efficient simulation can be created, making maximal use of the available hardware without constraining the model to require excessively specific resources.

UML as a cell and biochemistry modeling language

The systems biology community is building increasingly complex models and simulations of cells and other biological entities, and are beginning to look at alternatives to traditional representations such as those provided by ordinary differential equations (ODE). The lessons learned over the years by the software development community in designing and building increasingly complex telecommunication and other commercial real-time reactive systems, can be advantageously applied to the problems of modeling in the biology domain. Making use of the object-oriented (OO) paradigm, the unified modeling language (UML) and Real-Time Object-Oriented Modeling (ROOM) visual formalisms, and the Rational Rose RealTime (RRT) visual modeling tool, we describe a multi-step process we have used to construct top-down models of cells and cell aggregates. The simple example model described in this paper includes membranes with lipid bilayers, multiple compartments including a variable number of mitochondria, substrate molecules, enzymes with reaction rules, and metabolic pathways. We demonstrate the relevance of abstraction, reuse, objects, classes, component and inheritance hierarchies, multiplicity, visual modeling, and other current software development best practices. We show how it is possible to start with a direct diagrammatic representation of a biological structure such as a cell, using terminology familiar to biologists, and by following a process of gradually adding more and more detail, arrive at a system with structure and behavior of arbitrary complexity that can run and be observed on a computer. We discuss our CellAK (Cell Assembly Kit) approach in terms of features found in SBML, CellML, E-CELL, Gepasi, Jarnac, StochSim, Virtual Cell, and membrane computing systems.

Structure-based systems biology: a zoom lens for the cell

Systems biology seeks to explain complex biological systems, such as the cell, through the integration of many different types of information. Here, we discuss how the incorporation of high-resolution structural data can provide key molecular details often necessary to understand the complex connection between individual molecules and cell behavior. We suggest a process of zooming on the cell, from global networks through pathways to the precise atomic contacts at the interfaces of interacting proteins.

Evolvable social agents for bacterial systems modeling

We present two approaches to the individual-based modeling (IbM) of bacterial ecologies and evolution using computational tools. The IbM approach is introduced, and its important complementary role to biosystems modeling is discussed. A fine-grained model of bacterial evolution is then presented that is based on networks of interactivity between computational objects representing genes and proteins. This is followed by a coarser grained agent-based model, which is designed to explore the evolvability of adaptive behavioral strategies in artificial bacteria represented by learning classifier systems. The structure and implementation of the two proposed individual-based bacterial models are discussed, and some results from simulation experiments are presented, illustrating their adaptive properties.

Space as the final frontier in stochastic simulations of biological systems

Recent technological and theoretical advances are only now allowing the simulation of detailed kinetic models of biological systems that reflect the stochastic movement and reactivity of individual molecules within cellular compartments. The behavior of many systems could not be properly understood without this level of resolution, opening up new perspectives of using computer simulations to accelerate biological research. We review the modeling methodology applied to stochastic spatial models, also to the attention of non-expert potential users. Modeling choices, current limitations and perspectives of improvement of current general-purpose modeling/simulation platforms for biological systems are discussed.

A framework for whole-cell mathematical modeling

The default framework for modeling biochemical processes is that of a constant-volume reactor operating under steady-state conditions. This is satisfactory for many applications, but not for modeling growth and division of cells. In this study, a whole-cell modeling framework is developed that assumes expanding volumes and a cell-division cycle. A spherical newborn cell is designed to grow in volume during the growth phase of the cycle. After 80% of the cycle period, the cell begins to divide by constricting about its equator, ultimately affording two spherical cells with total volume equal to twice that of the original. The cell is partitioned into two regions or volumes, namely the cytoplasm (Vcyt) and membrane (Vmem), with molecular components present in each. Both volumes change during the cell cycle; Vcyt changes in response to osmotic pressure changes as nutrients enter the cell from the environment, while Vmem changes in response to this osmotic pressure effect such that membrane thickness remains invariant. The two volumes change at different rates; in most cases, this imposes periodic or oscillatory behavior on all components within the cell. Since the framework itself rather than a particular set of reactions and components is responsible for this behavior, it should be possible to model various biochemical processes within it, affording stable periodic solutions without requiring that the biochemical process itself generates oscillations as an inherent feature. Given that these processes naturally occur in growing and dividing cells, it is reasonable to conclude that the dynamics of component concentrations will be more realistic than when modeled within constant-volume and/or steady-state frameworks. This approach is illustrated using a symbolic whole cell model.

OptStrain: a computational framework for redesign of microbial production systems

This paper introduces the hierarchical computational framework OptStrain aimed at guiding pathway modifications, through reaction additions and deletions, of microbial networks for the overproduction of targeted compounds. These compounds may range from electrons or hydrogen in biofuel cell and environmental applications to complex drug precursor molecules. A comprehensive database of biotransformations, referred to as the Universal database (with >5700 reactions), is compiled and regularly updated by downloading and curating reactions from multiple biopathway database sources. Combinatorial optimization is then used to elucidate the set(s) of non-native functionalities, extracted from this Universal database, to add to the examined production host for enabling the desired product formation. Subsequently, competing functionalities that divert flux away from the targeted product are identified and removed to ensure higher product yields coupled with growth. This work represents an advancement over earlier efforts by establishing an integrated computational framework capable of constructing stoichiometrically balanced pathways, imposing maximum product yield requirements, pinpointing the optimal substrate(s), and evaluating different microbial hosts. The range and utility of OptStrain are demonstrated by addressing two very different product molecules. The hydrogen case study pinpoints reaction elimination strategies for improving hydrogen yields using two different substrates for three separate production hosts. In contrast, the vanillin study primarily showcases which non-native pathways need to be added into Escherichia coli. In summary, OptStrain provides a useful tool to aid microbial strain design and, more importantly, it establishes an integrated framework to accommodate future modeling developments.

On the use of qualitative reasoning to simulate and identify metabolic pathways

MOTIVATION

Perhaps the greatest challenge of modern biology is to develop accurate in silico models of cells. To do this we require computational formalisms for both simulation (how according to the model the state of the cell evolves over time) and identification (learning a model cell from observation of states). We propose the use of qualitative reasoning (QR) as a unified formalism for both tasks. The two most commonly used alternative methods of modelling biochemical pathways are ordinary differential equations (ODEs), and logical/graph-based (LG) models.

RESULTS

The QR formalism we use is an abstraction of ODEs. It enables the behaviour of many ODEs, with different functional forms and parameters, to be captured in a single QR model. QR has the advantage over LG models of explicitly including dynamics. To simulate biochemical pathways we have developed 'enzyme' and 'metabolite' QR building blocks that fit together to form models. These models are finite, directly executable, easy to interpret and robust. To identify QR models we have developed heuristic chemoinformatics graph analysis and machine learning procedures. The graph analysis procedure is a series of constraints and heuristics that limit the number of ways metabolites can combine to form pathways. The machine learning procedure is generate-and-test inductive logic programming. We illustrate the use of QR for modelling and simulation using the example of glycolysis.

AVAILABILITY

All data and programs used are available on request.

CESE

Cell electrophysiology simulation environment (CESE) is an integrated environment for performing simulations with a variety of electrophysiological models that have Hodgkin-Huxley and Markovian formulations of ionic currents. CESE is written in Java 2 and is readily portable to a number of operating systems. CESE allows execution of single-cell models and modification and clamping of model parameters, as well as data visualisation and analysis using a consistent interface. Model creation for CESE is facilitated by an object-oriented approach and use of an extensive modelling framework. The Web-based model repository is available.

AVAILABILITY

CESE and the Web-based model repository are available at http://cese.sourceforge.net/.